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Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI

Overview of attention for article published in Radiation Oncology, November 2016
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Title
Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI
Published in
Radiation Oncology, November 2016
DOI 10.1186/s13014-016-0718-3
Pubmed ID
Authors

Rakesh Shiradkar, Tarun K Podder, Ahmad Algohary, Satish Viswanath, Rodney J. Ellis, Anant Madabhushi

Abstract

Radiomics or computer - extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous ([Formula: see text]) which is the current clinical standard, radiomics based focal ([Formula: see text]), and whole gland with a radiomics based focal boost ([Formula: see text]). Comparison of [Formula: see text] against conventional [Formula: see text] revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. [Formula: see text] resulted in only a marginal increase in dosage to the OARs compared to [Formula: see text]. A similar trend was observed in case of EBRT with [Formula: see text] and [Formula: see text] compared to [Formula: see text]. A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 <1%
Unknown 141 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 19%
Student > Ph. D. Student 20 14%
Student > Master 18 13%
Other 10 7%
Student > Bachelor 7 5%
Other 17 12%
Unknown 43 30%
Readers by discipline Count As %
Medicine and Dentistry 33 23%
Engineering 14 10%
Physics and Astronomy 12 8%
Computer Science 9 6%
Agricultural and Biological Sciences 5 4%
Other 14 10%
Unknown 55 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 November 2016.
All research outputs
#18,480,433
of 22,899,952 outputs
Outputs from Radiation Oncology
#1,416
of 2,060 outputs
Outputs of similar age
#236,926
of 312,766 outputs
Outputs of similar age from Radiation Oncology
#16
of 34 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,060 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,766 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.